handler/docs/local-models.md

Local model backends (Qwen-Coder & friends)

Handler can run agents on locally-hosted models without changing anything about how an agent works: it is still the same claude binary with the same generated settings.json, hooks, skills, MCP connectors, plugins, and permission gates. The only thing a model backend changes is the environment of that one agent's process:

Variable	From
ANTHROPIC_BASE_URL	the backend's base_url
ANTHROPIC_AUTH_TOKEN	the backend's stored API key (decrypted at launch; a placeholder when none is stored, so the subscription OAuth token is never sent to a local endpoint)
ANTHROPIC_MODEL	the backend's model
ANTHROPIC_SMALL_FAST_MODEL	small_fast_model, falling back to model
CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC	1 (skip sidecar calls a local endpoint won't serve; override via the row's env map)

Register backends on the dashboard's Claude → Models tab (or POST /claude/models), then pick one from the Model dropdown when spawning an agent. No selection = the worker's logged-in Claude subscription, exactly as before. The agent is pinned to its backend: resumes come back up on the same one, and deleting a backend makes resumes of its agents fail loudly rather than silently falling back to the subscription.

Why "tool calling not working" happens with Qwen-Coder

Claude Code speaks the Anthropic Messages API (POST /v1/messages): it sends tool definitions in Anthropic's schema and expects structured tool_use content blocks back. Local servers — Ollama, llama.cpp's llama-server, LM Studio, vLLM's default OpenAI mode — speak the OpenAI Chat Completions API instead. Point ANTHROPIC_BASE_URL at one of those and the request either 404s or, with a naive translator in between, the model's tool calls come back as plain text (Qwen emits its own XML-ish <tool_call> format) that Claude Code can't execute. That is the whole failure: the model is fine, the dialect in the middle is wrong.

Two things must both be true:

1. The endpoint must serve the Anthropic Messages API, translating to whatever your server speaks.
2. The inference server must parse the model's native tool-call format into structured tool calls — for Qwen that means a Qwen-aware parser/template, not the default one.

Working stacks

Recommended: vLLM (Qwen tool parser) + LiteLLM (Anthropic translation)

vLLM parses Qwen's tool-call format natively when told to:

# Qwen3-Coder
vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --port 8000

# Qwen2.5-Coder uses the hermes parser instead:
#   --tool-call-parser hermes

LiteLLM in front exposes the Anthropic /v1/messages endpoint:

# litellm-config.yaml
model_list:
  - model_name: qwen3-coder-30b
    litellm_params:
      model: hosted_vllm/Qwen/Qwen3-Coder-30B-A3B-Instruct
      api_base: http://127.0.0.1:8000/v1
general_settings:
  master_key: sk-local-anything

litellm --config litellm-config.yaml --port 4000

Then register the backend in Handler: base URL http://<host>:4000, model qwen3-coder-30b, API key sk-local-anything.

llama.cpp / Ollama

• llama-server needs --jinja (and, for Qwen, a chat template with tool support — recent official Qwen GGUFs ship one; older community quants often don't, which is another common source of "tools don't work").
• Ollama supports OpenAI-style tool calling for models whose Modelfile template declares it; check ollama show <model> --template mentions .Tools before blaming the proxy.
• Either way, they still only speak OpenAI-dialect — keep LiteLLM (use ollama_chat/<model>, not ollama/<model>, for tool support) or claude-code-router in front as the Anthropic translator.

Expectations and tips for small models

• Keep the harness light. Handler's agents run tool-heavy (hooks, MCP connectors, skills). A 7B model will fumble that loop; Qwen3-Coder-30B-class models handle it reasonably. Disable connectors the agent doesn't need and keep tasks small and concrete.
• Raise timeouts, cap output. The row's env map is the escape hatch: API_TIMEOUT_MS=600000, CLAUDE_CODE_MAX_OUTPUT_TOKENS=8192 are sensible for a local 30B.
• The gates don't relax. The Stop/PreToolUse hooks still block un-tested, un-pushed work regardless of which model produced it — that's the point of keeping the same binary.
• The subscription is untouched. The web login, credential sync, and every agent spawned without a model selection keep working exactly as before; backends are purely additive.